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Abstract:

The venturi scrubber has been used as air pollution controlling device. These scrubbers are promising device for cleaning the contaminated gases. It is found in the literature that the performance of venturi scrubber (i.e. collection efficiency), is significantly influenced by droplet distribution, pressure drop, disintegration of liquid, droplet sizes and injection methods. Effect of submergence height, multi-stage injection, position of the orifice, diameter of orifice, throat length and angle of convergence and divergence of venturi scrubber is found scarce and these parameters are affecting collection efficiency drastically. Therefore, it is necessary to study their effect to improve the performance of self-priming venturi scrubber. This article is the review of numerical and experimental study of the performance in venturi scrubber.

Keywords:

Venturi Scrubber,Self-Priming,CFD Modelling,Collection efficiency,

Refference:

I.A.Rahimi,J.Fathikalajahi,andM.Taheri,“ANewMethodofEddyDiffusivityCalculationforDropletsofaVenturiScrubber,”vol.84,no.February,pp.310–315,2006.

II.A.Moharana,P.Goel,andA.K.Nayak,“N12:Performanceestimationofaventuriscrubberanditsapplicationtoself-primingoperationindecontaminatingaerosolparticulates,”Nucl.Eng.Des.,vol.320,pp.165–182,2017.

III.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinlargescaleventuriscrubber.PartII.Dropletsize,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.424–431,2009.

IV.A.SharifiandA.Mohebbi,“AcombinedCFDmodelingwithpopulationbalanceequationtopredictpressuredropinventuriscrubbers,”2013.

V.A.M.Silva,J.C.F.Teixeira,andS.F.C.F.Teixeira,“Experimentsinalarge-scaleventuriscrubber.PartI:Pressuredrop,”Chem.Eng.Process.ProcessIntensif.,vol.48,no.1,pp.59–67,2009.

VI.A.Majid,Y.Changqi,S.Zhongning,W.Jianjun,andG.Haifeng,“CFDsimulationofdustparticleremovalefficiencyofaventuriscrubberinCFX,”Nucl.Eng.Des.,vol.256,pp.169–177,2013.

VII.A.Majid,C.Yan,S.Zhongning,J.Wang,andA.Rasool,“N6:CFDSimulationofThroatPressureinVenturiScrubberMajidAli,”Appl.Mech.Mater.,vol.173,pp.3630–3634,2012.

VIII.A.Rahimi,A.Niksiar,andM.Mobasheri,“Consideringrolesofheatandmasstransferforincreasingtheabilityofpressuredropmodelsinventuriscrubbers,”Chem.Eng.Process.ProcessIntensif.,vol.50,no.1,pp.104–112,2011.

IX.C.Goniva,Z.Tukovic,C.Feilmayr,T.Bürgler,andS.Pirker,“SimulationofoffgasscrubbingbyacombinedEulerian-Lagrangianmodel,”SeventhInt.Conf.CFDMiner.ProcessInd.,no.December,pp.1–7,2009.

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XII.F.AhmadvandandM.R.Talaie,“CFDmodelingofdropletdispersioninaVenturiscrubber,”Chem.Eng.J.,vol.160,no.2,pp.423–431,2010.

XIII.H.E.Hesketh,“FineParticleCollectionEfficiencyRelatedtoPressureDrop,ScrubbantandParticleProperties,andContactMechanism,”J.AirPollut.ControlAssoc.,vol.24,no.10,pp.939–942,1974.

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XXVI.M.Ali,C.Q.Yan,Z.N.Sun,J.J.Wang,andK.Mehboob,“N5:CFDSimulationofPredictionofPressureDropinVenturiScrubber,”Appl.Mech.Mater.,vol.166–169,pp.3008–3011,2012.

XXVII.M.Costa,A.Riberio,E.Tognetti,M.Aguiar,J.Gonclaves,andJ.Coury,“Performanceofaventuriscrubberintheremovaloffinepowderfromaconfinedgasstream,”Mater.Res.,vol.18,no.2,pp.177–179,2005.

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XXXII.N.Horiguchi,H.Yoshida,andY.Abe,“N9:Numericalsimulationoftwo-phaseflowbehaviorinVenturiscrubberbyinterfacetrackingmethod,”Nucl.Eng.Des.,vol.310,pp.580–586,2016.

XXXIII.N.Horiguchi,H.Yoshida,S.Uesawa,A.Kaneko,andY.Abe,“Icone21-16287FilterVenting:PreliminaryAnalysisandObservationofHydraulic,”pp.1–6,2013.

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LV.Y.Zhou,Z.Sun,H.Gu,andZ.Miao,“Performanceofiodidevapourabsorptionintheventuriscrubberworkinginself-primingmode,”Ann.Nucl.Energy,vol.87,pp.426–434,2016.

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Abstract:

The most challenging task is searching Telugu script from the database because of difficulty in differentiating the Characteristics of the Telugu word or scripts. In this, we introduced robust approach for Telugu script retrieval using transformation-based methodology. Non-subsampled contourlet transform (NSCT) is utilized for texture classification which will function based on Non-subsampled pyramid filter bank (NSPFB) and Non-subsampled directional filter bank (NSDFB). Spatial dependence matrix is utilized to extract the texture features. In addition, image statistics is computed to enhance the retrieval performance further. Finally, hamming similarity metric is calculated which calculates the distance between trained and test word templates, which an effective distance metric over conventional Euclidean distance. In order to test, missing segment, noisy, corrupted and occlusion effected words are used as an input and taken into consideration multi conjunct vowel consonant clustered word images for showing the robustness of presented algorithm. In the substantial simulation analysis gives the presented technique finds most similar word images from database although if it is under testing conditions. Our presented scheme has superior performance compared to the traditional approaches described in the literature with respect to mean Average Precision (mAP) and mean Average Recall (mAR).

Keywords:

Telugu script,texture features,statistical properties,non-subsampled contourlet transform,statistical parameters,feature vector and hammingdistance metric,

Refference:

I.Arthur L. da Cunha, Jianping Zhou,and Minh N. Do, “The Non-subsampled Contourlet Transform: Theory, Design and Applications”, IEEE Transaction on Image Processing, Vol. 15, No. 10, pp. 3089-3100, 2006.

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XII.J. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel Codebooks for Scene Categorization”, In: Proc. Of European Conf. on Computer Vision, Berlin, Heidelberg, pp. 696-709, 2008.

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XVI.K. Takeda, K. Kise, and M. Iwamura, “Real-time document image retrieval for a 10 Million pages database with a memory efficient and stability improved LLAH”, International Conf. on Document Analysis and Recognition, Beijing, China, pp. 1054-1058, 2011.

XVII.K.Mohana Lakshmi, Dr.T.Ranga Babu, “A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming , ICCCPE(Springer LNS), 978-981-13-0211-4, 2018.

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Abstract:

Transportation model provides a powerful framework to meet the Business challenges. In highly competitive market the pressure is increasing rapidly to the organizations to determine the better ways to deliver goods to the customers with minimum transportation cost. In this paper we proposed a new algorithm based on Least Cost Method(LCM)for finding Initial Basic Feasible Solution(IBFS) to minimize transportation cost .Our proposed algorithm provides a IBFS which is either optimal or near to the optimal value with minimum steps comparatively better than those obtain by traditional algorithm or method .For the validity of this algorithm we considered a numerical transportation problem and comparative study has been made minimum cost with graphically.

Keywords:

Transportation Cost, Least Cost Method, Supply,Demand, Initial Basic feasible Solution,Optimum solution,

Refference:

I.Ahuja, R.K.(1986). Algorithms for minimax transportation problem. Naval Research Logistics Quarterly.33 (4), 725-739. II.A.Gupta, S.Khanna and M. Puri, (1992), Paradoxical situations in transportation problems, Cahiers du Centre d’Etudes de RechercheOperationnell, 37–49.

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Abstract:

This letter presents a single feed novel miniaturized patch antenna for WiMax applications and small wireless technologies. Antenna is fabricated on FR4 substrate with 1.6mm thickness and copper sheet of 0.035mm. The miniaturization of 82% is achieved by etching a Fork shape slot in ground plane as response is observed at 3.4GHz. Simulated and measured results shows acceptable gain of 3.4 to 3.6dB and efficiency ranging to 82% with 260MHz bandwidth. The proposed antenna is simulated in Computer Simulation Technology 2015. The measurement results demonstrate that the proposed antenna provides acceptable radiation performances with directional radiation patterns at desired frequency.

Keywords:

Miniaturization,Microstrip Patch Antenna (MPA),directivity,gain,bandwidth,Slots,Computer Simulation Technology (CST),

Refference:

I.Aguilar, Suzette M., Mudar A. Al-Joumayly, Matthew J. Burfeindt, Nader Behdad, and Susan C. Hagness. ”Multiband miniaturized patch antennas for a compact, shielded microwave breast imaging array.” IEEE transactions on antennas and propagation 62, no. 3 (2014): 1221-1231.

II.Ali, M. S. M., Rahim, S. K. A., Sabran, M. I., Abedian, M., Eteng, A., Islam, M. T. (2016). Dual band miniaturized microstrip slot antenna for WLAN applications. Microwave and Optical Technology Letters, 58(6), 1358-1362.

III.Amit K. Singh*, Mahesh P.Abegaonkar, and Shiban K. Koul, “Miniaturized Multiband Microstrip Patch Antenna Using Metamaterial Loading for Wireless Application” Progress In Electromagnetics Research C, Vol. 83, 71–82, 2018.

IV.Boukarkar, Abdelheq, Xian Qi Lin, Yuan Jiang, and Yi QiangYu. “Miniaturized single-feed multiband patch antennas.” IEEE Transactions on Antennas and Propagation 65, no. 2 (2017): 850-854.

V.Chen, Richard H., and Yi-Cheng Lin. “Miniaturized design of microstrip-fed slot antennas loaded with C-shaped rings.” IEEE Antennas and Wireless Propagation Letters 10 (2011): 203-206.

VI.Fritz-Andrade, E., Tirado-Mendez, J. A., Jardon-Aguilar, H., & Flores-Leal, R. (2017). Application of complementary split ring resonators for size reduction in patch antenna arrays. Journal of Electromagnetic Waves and Applications, 31(16), 1755-1768.

VII.Gupta, Ashish. “Miniaturized dual‐band metamaterial inspired antenna with modified SRR loading.” International Journal of RF and Microwave Computer‐Aided Engineering (2018): e21283.

VIII.Li, Ziyang, Leilei Liu, Pinyan Li, and Jian Wang. “Miniaturized design of CPW-Fed slot antennas using slits.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

IX.M. M. Bait-Suwailam and H. M. Al-Rizzo, “Size reduction of microstrip patch antennas using slotted Complementary Split-Ring Resonators,” in Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, 2013, pp. 528-531.

X.Motevasselian, Alireza, and William G. Whittow. “Miniaturization of a Circular Patch Microstrip Antenna Using an Arc Projection.” IEEE Antennas and Wireless Propagation Letters 16 (2017): 517-520.

XI.Saad Hassan Kiani, Khalid Mahmood, Mehre Munir and Alex James Cole, “A Novel Design of Patch Antenna using U-Slotand Defected Ground Structure” International Journal of Advanced Computer Science and Applications(ijacsa),8(3),2017. http://dx.doi.org/10.14569/IJACSA.2017.080303E.

XII.Tirado‐Mendez, J. A., Jardon‐Aguilar, H., Flores‐Leal, R., & Rangel‐Merino, A. (2018). Multiband reduced‐size patch antenna by employing a modified DMS‐spur‐line combo technique. International Journal of RF and Microwave Computer‐Aided Engineering, 28(4), e21232.

XIII.Wang, Qian, Ning Mu, Linli Wang, Jingping Liu, and Ying Wang. “Miniaturization microstrip antenna design based on artificial electromagnetic structure.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

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Abstract:

This study is a very good approach to find the solution of secure authentication for IOT based smart home environment and its appliances. The study aims to compare the different authentication methods with respect to smart home environment and trying to identify its limitation. After analyzing the existing authentication methods its limitation and core issues then targeted the message authentication for SHE. Presently SHE authentication is based on Exchange of six message authentication techniques in Enhance authentication and key establishment scheme 6LOWPAN (EAKES6Lo) which is advance version of secure authentication and key establishment scheme (SAKES). This authentication method cause much high end to end delay, energy consumption, overall throughput of the system, complexity and poor security approach. By simulation of EAKES6Lo and SAKES scheme found some results, in contrast to these results, there may be another solution to access any SHE lights, fans, refrigerators, air condition, geezer, door lock, microwave oven, television and water pump, HVAC control and security alarms etc remotely with better security, better complexity, minimum energy consumption, better key length, better throughput and minor end to end delay named two step authentication (TSA). The proposed model also helps to monitor accessing system by comparing security codes and its complexity.

Keywords:

Internet of Things(IOT),Smart Home Environment (SHE),Version 6 Low Power Wireless Personal Area Network (6LoWPAN),Enhanced Authentication and Key Establishment Scheme for 6LoWPAN (EAKES6Lo),Secure Authentication and Key Establishment Scheme(SAKES),Two Step Authentication(TSA),

Refference:

I.Atzori, L., Iera, Antonio,Morabito, Giacomo, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.

II.Commission, E., The alliance for internet of things innovation (AIOTI). 2016.

III.Costin Badic ̆ a ̆, M.B., Amelia Badic ̆, a ̆, An Overview of Smart Home Environments: Architectures, Technologies and Applications. 2017: p. 8.

IV.Ding, F.S., A.; Tong, E.;Li,J., A smart gateway architecture for improving effeciency of home network application. 2016.

V.Geoff Mulligan , M.y., Patrick Wetterwal, ColinPatrickO’Flyn, MakingsensornetworksIPv6ready. 2008.

VI.Huichen Lin, N.W.B., IoT Privacy and Security Challenges for Smart Home Environments. 2016(4 July 2016).

VII.Internet, ADVANCE AUTHENTICATION TECHNIQUES.

VIII.Kenji, I.M., T.; Toyoda, K.; Sasase, I, Secure parent node selection scheme in route construction to exclude attacking nodes from rpl network. 2015. 4: p.5.

IX.Komninos, N., Phillppou, E. & Pitsillides, A. , Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures 2014.

X.Madakam, S., R. Ramaswamy, and S. Tripathi, Internet of Things (IoT): A Literature Review. IT Applications Group, 2015 3: p. 164-173.

XI.Mangal Sain, Y.J.K., Hoon Jae Lee, Survey on Security in Internet of things: state of the art and challenges 2014.

XII.Md. Alam Hossain, M.B.H., Md. Shafin Uddin, Shariar Md. Imtiaz MD6 Message Digest Algoritham. Reasearch Gate, 2016.16.

XIII.Rescorla, E.M., N., Datagram Transport Layer Security. Internet Engineering task force, 2012.

XIV.Sandeep Kumar Rao, D.M., Dr. Danish Ali Khan, A Survey on Advanced Encryption Standard 2017.

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Abstract:

With the passage of time, the demand of electricity is increasing day by day. The conventional electricity resources are getting depleted because of limited reserves of coal, natural gas and oil. Also most of the electricity resources are not environmental friendly. There was a need to design a mechanism that can be used as an alternative resource for the production of electricity that can be environmental friendly as well as a cheap source of generation. In the last couple of years, it is indicated that energy obtained from the sun can be the best alternate resource for energy. In this research work, the system design approach based on the Maximum Power Point Tracking (MPPT) Controller has been designed. This approach is utilized for extracting maximum available power from PV module through simulation in protius software. This system is quite efficient, effective and has high performances. Buck and boost converter have been utilized for better efficiency.

Keywords:

Electricity,Renewable Energy,Solar Charge Controller, Maximum Power Point Tracking,

Refference:

I.A. Ali, Y. Wang, W. Li and X. He, “Implementation of simple moving voltage average technique with direct control incremental conductance method to optimize the efficiency of DC microgrid,” in Emerging Technologies (ICET), 2015 International Conference on, 2015.

II.A. Argentiero, C. A. Bollino, S. Micheli and C. Zopounidis, “Renewable energy sources policies in a Bayesian DSGE model,” Renewable Energy, vol. 120, pp. 60-68, 2018.

III.A. Naserbegi, M. Aghaie, A. Minuchehr and G. Alahyarizadeh, “A novel exergy optimizationof Bushehr nuclear power plant by Gravitational Search Algorithm (GSA),” Energy, 2018.

IV.A. Soetedjo, A. Lomi and B. J. Puspita, “A Hardware Testbed of Grid-Connected Wind-Solar Power System,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 1, pp. 52-56, 2018.

V.A. M. Atallah, A. Y. Abdelaziz and R. S. Jumaah, “Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters,” Emerging Trends in Electrical, Electronics & Instrumentation Engineering: An international Journal (EEIEJ), vol. 1, pp. 31-44, 2014.

VI.B. Gjorgiev and G. Sansavini, “Electrical power generation under policy constrained water-energy nexus,” Applied Energy, vol. 210, pp. 568-579, 2018.

VII.F. Zhou, Y.-F. Chang, B. Fowler, K. Byun and J. C. Lee, “Stabilization of multiple resistance levels by current-sweep in SiOx-based resistive switching memory,” Applied Physics Letters, vol. 106, p. 063508, 2015.

VIII.J. Ahmed and Z. Salam, “An improved perturb and observe (P&O)maximum power point tracking (MPPT) algorithm for higher efficiency,” Applied Energy, vol. 150, pp. 97-108, 2015.

IX.K. Khanafer and K. Vafai, “A review on the applications of nanofluids in solar energy field,” Renewable Energy, 2018.

X.K. Ishaque, Z. Salamand G. Lauss, “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions,” Applied Energy, vol. 119, pp. 228-236, 2014.

XI.K. S. Tey and S. Mekhilef, “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level,” Solar Energy, vol. 101, pp. 333-342, 2014.

XII.L.-L. Li, G.-Q. Lin, M.-L. Tseng, K. Tan and M. K. Lim, “A Maximum Power Point Tracking Method for PV System with Improved Gravitational Search Algorithm,” Applied Soft Computing, 2018.

XIII.M. Peng, Y. Li, Z. Zhao and C. Wang, “System architecture and key technologies for 5G heterogeneous cloud radio access networks,” IEEE network, vol. 29, pp. 6-14, 2015.

XIV.P. Sivakumar, A. A. Kader, Y. Kaliavaradhan and M. Arutchelvi, “Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions,” Renewable Energy, vol. 81, pp. 543-550, 2015.

XV.P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 309-313, 2015.

XVI.R. Kardooni, S. B. Yusoff, F. B. Kari and L. Moeenizadeh, “Public opinion on renewable energy technologies and climate change in Peninsular Malaysia,” Renewable Energy, vol. 116, pp. 659-668, 2018.

XVII.R. M. Linus and P. Damodharan, “Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems,” IET Renewable Power Generation, vol. 9, pp. 682-689, 2015.

XVIII.R. Cheng and Y. Jin, “A social learning particle swarmoptimization algorithm for scalable optimization,” Information Sciences, vol. 291, pp. 43-60, 2015.

XIX.S. Dincer and I. Dincer, “Comparative Evaluation of Possible Desalination Options With Various Nuclear Power Plants,” in Exergetic, Energetic and Environmental Dimensions, Elsevier, 2018, pp. 569-582.

XX.S. Krauter, “Simple and effective methods to match photovoltaic power generation to the grid load profile for a PV based energy system,” Solar Energy, vol. 159, pp. 768-776, 2018.

XXI.S. Carley, “State renewable energy electricity policies: An empirical evaluation of effectiveness,” Energy policy, vol. 37, pp. 3071-3081, 2009.

XXII.S. P. Ayeng’o, T. Schirmer, K.-P. Kairies, H. Axelsen and D. U. Sauer, “Comparison of off-grid power supply systems using lead-acid and lithium-ion batteries,” Solar Energy, vol. 162, pp. 140-152, 2018.

XXIII.S. Kiranyaz, T. Ince and M. Gabbouj, “Particle swarm optimization,” in Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Springer, 2014, pp. 45-82.

XXIV.T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on energy conversion, vol. 22, pp. 439-449, 2007.

XXV.V. Salas, E. Olias, A. Barrado and A. Lazaro, “Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems,” Solar energy materials and solar cells, vol. 90, pp. 1555-1578, 2006.

XXVI.Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001.

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Abstract:

Social media data made real world like a web of data which is highly categorical in nature. Data having categorical attributes are omnipresent in existing real world. Clustering is an effective approach to deal with categorical data. However, partitional clustering algorithms are prone to fall into local optima for categorical data. A novel approach of ABC K-modes has been proposed to address this issue but acceleration issue of this algorithm was still a challenge for it. In this paper, we address this challenge to reduce the acceleration factor of algorithm and proposing a novel modified ABC K-modes approach which we refer as N-ABC K-modes approach. In our approach, unlike existing ABC K-modes we introduces different attribute matrix for each data sets. In further step, we apply XOR operation to combine the matrix of similar attributes. In last phase, dissimilar data would form a cluster and we apply clustering follow by searching on this cluster. The performance of New ABC K-modes evaluated by a series of tests and experiments over real time streaming social media data like twitter and facebook in comparison with that of other popular algorithms for categorical data.

Keywords:

Big data,Twitter,Clustering,Big data Analysis,Artificial Bee Colony(ABC), Data classification,

Refference:

I.Arthur, D., &Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In N. Bansal, K.Pruhs, & C. Stein (Eds.), Proc. of the eighteenth anual ACMSIAM symposium on discrete algorithms, SODA (pp. 1027–1035).

II.Han J, Kamber M, Pei J. Data mining concepts and techniques. 3rd ed. Waltham: Morgan Kaufmann; 2012.

III.Handl, J., Knowles, J., &Dorigo, M. (2006). Ant-based clustering and topographic mapping. Artificial Life, 12(1), 35–62.

IV.Hruschka, E., Campello, R., & de Castro, L. (2006). Evolving clusters in gene-expression data. Information Sciences, 176(13), 1898–1927.

V.Huang Z. Clustering large data sets with mixed numeric and categorical values. In the first Pacific-Asia Conference on Knowledge Discovery and Data Mining. 1997; pp. 21–34.

VI.Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery. 1998; 2: 283–304.

VII.Ikura Y, Gimple M. Efficient scheduling algorithms for a single batch processing machine. Operations Research Letters. 1986; 5: 61–65.

VIII.Ji J, Pang W, Zheng Y, Wang Z, Ma Z (2015) A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data. PLoS ONE 10(5): e0127125. doi:10.1371/journal.pone.0127125.

IX.Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2004). A local search approximation algorithm for k-meansclustering. Computational Geometry, 28(2–3), 89–112.

X.Kao Y-T, Zahara E, Kao IW. A hybridized approach to data clustering. Expert Systems with Applications.2008; 34: 1754–1762.

XI.Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing.2008; 8: 687–697.XII.Karaboga D, Ozturk C. A novel clustering approach: artificial bee colony (ABC) algorithm. Applied Soft Computing. 2011; 11: 652–657.

XIII.Li, L., Yang, Y., Peng, H., & Wang, X. (2006). An optimization method inspired by chaotic ant behavior. International Journal of Bifurcation and Chaos, 16, 2351–2364.

XIV.Luo C, Pang W, Wang Z (2014) Semi-Supervised clustering on heterogeneous information networks. In: Proceedings of 18th Pacific Asia Conference of Knowledge Discovery and Data Mining (PAKDD’14). Taiwan, pp 548-559.

XV.MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability (pp. 281–297).

XVI.Rand WM. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association. 1971; 66: 846–850.

XVII.Shamanth Kumar, Fred Morastatter, Huan Liu, Twitter Data analytics, Springer, Aug 19,2013.

XVIII.Shelokar PS, Jayaraman VK, Kulkarni BD. An ant colony approach for clustering. AnalyticaChimicaActa. 2004; 509: 187–195.

XIX.Teodorović D. Bee Colony Optimization (BCO). In: Lim C, Jain L, Dehuri S, editors. Innovations in Swarm Intelligence. Berlin: Springer-Verlag; 2009. pp. 39–60.

XX.Van der Merwe, D. W., &Engelbrecht, A. P. (2003). Data clustering using particle swarm optimization. In Proceedings of IEEE congress on evolutionary computation (pp. 215–220).

XXI.Wan M, Li L, Xiao J, Wang C, Yang Y. Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems. 2012; 38: 321–341.

XXII.Wan, M., Li, L., Xiao, J., Yang, Y., Wang, C., &Guo, X. (2010). CAS based clustering algorithm for web users. Nonlinear Dynamics, 61(3), 347–361.

XXIII.Yang Y. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval. 1999; 1: 67–88.

XXIV.Zhang C, Ouyang D, Ning J. An artificial bee colony approach for clustering. Expert Systems with Applications.2010; 37: 4761–4767.

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Abstract:

We study the SIR model for the mathematical modeling of diseases of greater Noakhali. This model describes the spread of infectious diseases in which an individual may move from susceptible to infected and to recover. We discussed the mathematics behind the model and various tools for judging effectiveness in a certain territory. We completed the paper with an example using the infectious diseases, Pneumonia and Dysentery, commonly the children are infected. The current results of this paper are greatly instructive for us to further understand the epidemic spreading and design some fruitful prevention and disposal strategies to fight the epidemics.

Keywords:

SIR Model,Effective removal rate,Basic reproductive ratio,Effective reproductive ratio,

Refference:

I.Civil Surgeon Office Noakhali and Population &Housing Census 2011, Zila Report: Noakhali, Bangladesh Statistical Bureau, Bangladesh.

II.Civil Surgeon Office Lakhshmipur and Population & Housing Census 2011. Zila Report: Lakhshmipur, Bangladesh Statistical Bureau, Bangladesh.

III.Hackborn, Bill. Susceptible, Infected, Recovered: the SIR model of an Epidemic. University of Alberta: Augustana. Fall 2008.

IV.Hartl, Daniel (2007).Principles of Population Genetics.Sinauer Associates. p.45., ISBN978-0-87893-308-2.

V.School of Public Health. Concepts for the Prevention and Control of Microbial Threats. Center for Infectious Diseases and Emergency Readiness. June 2006. University of California Berkeley.

VI.Smith, David, and L. Moore. The SIR Model for Spread of Disease. MathDL. Dec. 2001. MMA. Fall 2008.

VII.T. D. Murray, Mathematical biology, Third edition, Springer-Verlag New York Berlin Heidelberg.

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Abstract:

The present study deals with the applicability and effectiveness of the algorithm of generalized Kudryashov method (GKM), which is one of the most workable methods to constitute the exact traveling wave solutions of non-linear evolution equations (NLEEs) in physical and mathematical science. The recent paper, we enucleated this method for each of the following Couple Boiti-Leon-Pempinelli equations system, DSSH equation and fourth-order nonlinear Ablowitz-Kaup-Newell-Segur (AKNS) water wave dynamical equation. The prominent competence of this method is to naturalize the way of solving systems of NLEEs. Moreover, we can see that when the parameters are ascribed to the particular values, obtain solitary wave solution from the exact travelling wave solution. The obtained new solutions have a wide range of inflictions in the field of physics and other areas of applied science. To perceive the physical phenomena, we have plotted coupled with some 2𝐷 and 3𝐷 graphical patterns of analytic solutions obtained in this study by using computer programming wolfram Mathematica. The worked-out solutions ascertained that the suggested method is effectual, simple and direct and can be exerted to several types of nonlinear systems of partial differential equations.

Keywords:

The generalized Kudryashov method, Couple Boiti-Leon-Pempinelli equations, DSSH equation, fourth-order nonlinear AKNS equation,travelingwave solution,exact solution,

Refference:

.A. Akbulut, M. Kaplan, F. Tascan, “The investigation of exact solutions of nonlinear partial differential equations by using exp (–𝜑𝜉)method”, Optik-Int. J. Light Elect. Optics, Vol.: 132, pp.: 382-387, 2017.

II.A. Ali, A. R. Seadawy, D. Lu, “Computational methods and traveling waves solutions for the fourth-order nonlinear Ablowitz-Kaup-Neweel-Segur water wave dynamical equation via two methods and its application”,Open Phys., Vol.: 16, Issue: 1, pp.: 219-226, 2018.

III.A. Bekir, A. Boz, “Applicationof He’s exp-function method for nonlinear evolution equations”,Comp. Math. Appl., Vol.: 58, Issue: 11-12, pp.: 2286-2293, 2009.

IV.A. Bekir, M. Kaplan, “Exponential rational function method for solving nonlinear equations arising in various physical models”,Chinese J. Phys., Vol.: 54, Issue: 3, pp.: 365-370, 2016.

V.A. Bekir, M. Kaplan, O. Guner, “A novel modified simple equation method and its application to some nonlinear evolution equation system”, 2ndInt. Conf. Analy. Appl. Math., Vol.:1611, Issue: 30, 2014.

VI.A. K. M. K. S. Hossain, M. A. Akbar, “Closed form solutions of two nonlinear equation via the enhanced (𝐺′/𝐺)-expansion method”, Cogent Math., Vol.: 4, ID: 1355958, 2017.

VII.A. M. Wazwaz,”A Sine-Cosine method for handling nonlinear wave equations”,Math. Comp. Modell., Vol.: 40, Issue: 5-6, pp.: 499-508, 2004.

VIII.A. M. Wazwaz,”The simplified Hirota’s method for studying three extended higher-order KdV-type equation”,J. Ocean Eng. Sci., Vol.: 1, Issue: 2, pp.:181-185, 2016.

IX.A. Sonmezoglu, M. Ekici, A. H. Arnous, Q. Zhou, H. Triki, S. P. Moshokoa, M. Z. Ullah, A. Biswas, M. Belic, “Embedded solitons with 𝑥2and𝑥3nonlinear susceptibilities by extended trial equation method”,Optik –Int. J. Light Elec. Optics,Vol.: 154, pp.: 1-9, 2018.

X.D. Kumar, A. R. Seadawy, A. K. Joardar, “Modified Kudryashov method via new exact solutions for some conformable fractional differential equations arising in mathematical biology”,Chinese J. Phys., Vol.: 56, Issue: 1, pp.: 75-85, 2018.

XI.D. U. Zhong, B. O. Tian, X. Y. Xie,J. Chai, X. Y. Wu, “Backlund transformation and soliton solutions in terms of the wronskian for the Khdomtsev-petviashvili-based system in fluid dynamics”,Pramana J. Phys., Vol.: 90, Issue: 45, pp.:1-6, 2018.

XII.E. Fan, “Extended tanh method and its application to nonlinear equations”,Phys. Lett. A, Vol.: 277, Issue: 4-5, pp.: 212-218, 2000.

XIII.E. Fan, J. Zhang, “Application of the Jacobi elliptic function method to special type nonlinear equations”,Phys. Lett. A, Vol.: 305, Issue: 6, pp.: 383-392, 2002.

XIV.E. M. E. Zayed, A. G. A. Nowehy, “Solitons and other exact solutions for a class of nonlinear Schrodinger type equation”,Optik-Int. J. Light Elec. Optics, Vol.: 130, pp.:1295-1311, 2017.

XV.E. M. E. Zayed, K. A. E. Alurrfi, “Homogeneous balance method and itsapplication for finding the exact solution for nonlinear evolution equation”,Ital. J. Pure Appl. Math., Vol.: 33, pp.: 307-318, 2014.

XVI.F. Mahmud, M. Samsuzzoha, M. A. Akbar, “The generalized Kudryashov method to obtain exact traveling wave solutions of thePHI-four equation and the Fishers equation”, Results Phys., Vol.: 7, pp.: 4296-4302, 2017.

XVII.H. Bulut, S. S. Atas, H. M. Baskonus, “Some novel exponential function structures to the Cahn-Allen equation”,Cogent Phys., Vol.: 3, ID: 1240886, 2016.

XVIII.J. L. Zhang, M. L. Wang, Y. M. Wang, Z. D. Fang, “The improved F-expansion method and its applications”,Phys. Letters A, Vol.: 350, Issue:1-2, pp.:103-109, 2006.

XIX.K. A. Gepreel, T. A. Nofal, A. A. Alasmari, “Exact solutions for nonlinear integro-partial differential equations using the generalized Kudryashov method”,J. Egyp. Math. Soci., Vol.: 25, Issue: 4, pp.: 438-444, 2017.

XX.K. Khan, M. A. Akbar, N. H. M. Ali,”The modified simple equation method for exact and solitary wave solutions of nonlinear evolution equation-the GZK-BBM equation and Right-Handed non-commutative Burgers equation”, Int. Schol. Resear. Notices, Vol.: 2013, ID:146704, 2013.

XXI.K. Khan, M. A. Akbar, “The exp(–𝜑𝜉)-expansion method for finding travelling wave solution of Vakhnenko-Parkes equation”, Int. J. Dyna. Syst. Diff. Equ., Vol.: 5, Issue: 1, pp.: 72, 2014.

XXII.K. R. Raslan, T. S. E. Danfal, K. A. Ali, “New exact solutions of coupled generalized regularized long wave equation”,J. Egyp. Math. Soci., Vol.: 25, Issue: 4, pp.: 400-405, 2017.

XXIII.L. Jianbin, K.Yang, “The extended F-expansion method and exact solution of nonlinear PDEs”,Chaos Solit. Frac., Vol.: 22, Issue:1, pp.:111-121, 2004.

XXIV.M. A. Akbar, N. H. M. Ali, “An ansatz for solving nonlinear partial differential equation in mathematical physics”,Springer Plus, Vol.: 5, Issue: 24, pp.:1-13, 2016.

XXV.M. A. Khater, A. R. Seadawy, D.Lu, “Elliptic and solitary wave solutions for the Bogoyavlenskii equations system: Couple Boiti-Leon-Pempinelli equations system and time fractional Cahn-allen equation”,Results Phys., Vol.: 7, pp.: 2325-2333, 2017.

XXVI.M. K. Elboree, “The Jacobi elliptic function method and its application for two component BKP hieracy equations”,Comp. Math. Appl., Vol.: 62, Issue: 12, pp.: 4402-4414, 2011.

XXVII.M. koparan, M. Kaplan, A. Bekir, O. Guner, “A novel generalized Kudryashov method for exact solutions of nonlinear evolution equations”,AIP Conference Proc., Vol.: 1798, Issue: 1, 2017.

XXVIII.M. M. Kabir, A. Khajeh, E. Aghdam, A. Y. Koma, “Modified Kudryashov method for finding exact solitary wave solutions of higher order nonlinear equations”,Math. Meth. Appl. Sci., Vol.: 34, Issue: 2, pp.: 213-219, 2011.

XXIX.M. N. Alam, M. A. Akbar, “Some new exact traveling wave solutions to the Simplified MCH equation and the (1+1)-dimensional combined KdV-mKdV equations”,J. Assoc. Arab Univ. Basic Appl. Sci., Vol.: 17, pp.: 6-13, 2015.

XXX.M. S. Islam, K. Khan, M. A. Akbar, “Application of the improved F-expansion method with Riccati equation to find the exact solution of the nonlinear evolution equation”, J. Egypt. Math. Soci., Vol.: 25, Issue: 1, pp.: 13-18, 2017.

XXXI.N. Ahmed, S. Bibi, U. Khan, S. T. Mohyud-Din, “A new modification in the exponential rational function method for nonlinear fractional differential equation”,Eur. Phys. J. Plus, Vol.: 133, Issue: 45, pp.: 1-11, 2018.

XXXII.N. A. Kudryashov, “One method for finding exact solutions of nonlinear differential equations”,Commun. Nonl. Sci. Num. Simul., Vol.: 17, Issue: 6, pp.: 2248-2253, 2012.

XXXIII.O. Guner, A. Bekir, “Solving nonlinear space-time fractional differential equation via ansatz method”,Comp. Meth. Diff. Equ., Vol.: 6, Issue: 1, pp.: 1-11, 2018.

XXXIV.Q. Feng, B. Zheng, B., “Traveling wave solutions for three nonlinear equations by (𝐺′/𝐺)-expansion method”,WSEAS Trans. Comp., Vol.: 9, Issue: 3, pp.: 225-234, 2010.

XXXV.S. Bibi, S. T. Mohyud-Din, U. Khan, N. Ahmed, “Khater method for non-linear Sharma Tasso-Olever (STO) equation of fractional order”, Results in Phys., Vol.: 7, pp.:4440-4450, 2017.

XXXVI.S. T. Demiray,Y. Pandir, H. Bulut, “Generalized Kudryashov method for time fractional differential equations”,Abst. Appl. Analy., Vol.: 2014, ID: 901540, 2014.

XXXVII.S. T. Mohyud-Din, A. Yildirim, M. M. Hosseini, “Variational iteration method for initial and boundary value problems using He’s polynomials”, Int. J. Diff. Equ., ID 426213, 2010.

XXXVIII.W. Malfliet, “The tanh method: a tool for solving certain classes of nonlinear evolution and wave equations”,J. Comp. Appl. Math., Vol.: 164-165, Issue: 1, pp.: 529-541, 2004.

XXXIX.W. M. Taha, M. S. M. Noorani, “Application of the (𝐺′/𝐺)-expansion method to the generalized Fisher’s equation and modified equal width equation”,J. Assoc. Arab Univ. Basic Appl. Sci., Vol.:15, pp.: 82-89, 2014.

XL.Y. Pandir, Y. Gurefe, E. Misirli, “The extended trial equation method for some Time-Fractional differential equation”,Disc. Dyna. Nature Soci., Vol.: 2013, ID: 491359, 2013

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Abstract:

The La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) is categorized as a mixed ionic-electronic conducting oxide has found significant attention as cathode material in solid oxide fuel cells (SOFCs) operating at intermediate temperatures, 500-850oC. The performance of LSCF electrode is limited by the oxygen ion transport process at the surface, which is the rate determining step of oxygen reduction reaction. To enhance the oxygen surface exchange process of LSCF electrode, a nano-composite electrolyte is introduced at the surface, which substantially improves the electrochemical performance. The electrical conductivity relaxation technique (ECR) has been used to study the oxygen surface exchange kinetics of bare LSCF and coated with a mixture of Ce0.8Sm0.2O2-δ (SDC) and ZrO2.Y2O3 (Yttria-stabilized zirconia -YSZ) nano-powders in three different weight ratios, SDC:YSZ = 0.5:1, 1:1, 1:0.5. The chemical oxygen surface exchange coefficient kchem of surface modified specimens were derived with a one-parameter fitting process. The results show that the oxygen surface exchange kinetics of LSCF is affected by the SDC-YSZ coating and the average kchem values of SDC-YSZ coated LSCF increases by a factor 2 to 8 from 650 to 850 oC, respectively. It has been concluded that the high ionic conductive oxide coating improves the oxygen surface exchange kinetics of underlying LSCF mixed conducting oxide and consequently enhances the performance of electrochemical device such as solid oxide fuel cell.

Keywords:

SOFC,ECR,Nano-composite,Coating,

Refference:

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